Skip to content

Latest commit

 

History

History
92 lines (67 loc) · 3.44 KB

README.md

File metadata and controls

92 lines (67 loc) · 3.44 KB

Investigating Neural Fit Approaches for Sentence Embedding Model Paradigms

This repository contains the code for our paper accepted for ECAI 2023:

H. Balabin, A. G. Liuzzi, J. Sun, P. Dupont, R. Vanderberghe, and M.-F. Moens, “Investigating Neural Fit Approaches for Sentence Embedding Model Paradigms,” in Proceedings of the 26th European Conference on Artificial Intelligence, Kraków, Poland: IOS Press, Oct. 2023, pp. 165–173. doi: 10.3233/FAIA230267.

💪 Getting Started

The following figure provides an overview of the workflow of this project.

Data

The basis for this project is the publicly available Pereira dataset (F. Pereira et al., “Toward a universal decoder of linguistic meaning from brain activation,” Nat Commun, vol. 9, no. 1, p. 963, Mar. 2018) that can be downloaded here. Once the data for each subject is downloaded, make sure to place the data for experiment #2 for each subject in the data/raw/pereira folder, if you want to repeat the processing steps from scratch.

The raw data should be organized as follows:

- data
  └── raw
     └── pereira
        ├── M02
        │   └── data_384sentences.mat
        ├── M04
        │   └── data_384sentences.mat
        ...

An easier alternative for the subsequential parts of the analyses is to directly use the preprocessed dataset in HuggingFace.

Analyses

The key parts of the analyses can be replicated using the following CLI commands. Note that some steps might require previously preprocessed data, and you might need to switch to the right directory first cd src/sentencefmricomparison.

  • RSA 
      $ python3 cli.py perform-rsa
  • Neural encoding 
      $ python3 cli.py calculate-brain-scores-cv-wrapper
  • Linguistic probing 
      $ python3 cli.py perform-rsa-text-permutations

Add the --help flag to see the available options for each command.

Appendices

All file containing all appendices mentioned in the main text of the paper can be found in the appendices folder.

🚀 Installation

The most recent code can be installed directly from GitHub using:

$ git clone [email protected]:lcn-kul/sentencefmricomparison.git
$ cd sentencefmricomparison
$ pip install -e .

👋 Attribution

⚖️ License

The code in this package is licensed under the MIT License.

📖 Citation

H. Balabin, A. G. Liuzzi, J. Sun, P. Dupont, R. Vanderberghe, and M.-F. Moens, “Investigating Neural Fit Approaches for Sentence Embedding Model Paradigms,” in Proceedings of the 26th European Conference on Artificial Intelligence, Kraków, Poland: IOS Press, Oct. 2023, pp. 165–173. doi: 10.3233/FAIA230267.

💰 Funding

This research was supported by funding from the Research Foundation - Flanders (Fonds Wetenschappelijk Onderzoek, FWO) grants 1154623N, 1247821N and from the European Research Council (ERC) under Grant Agreement No. 788506.

🍪 Cookiecutter

This repository is partially based on @audreyfeldroy's cookiecutter package using @cthoyt's cookiecutter-snekpack template.